Filled-in Document Identification Using Local Features and a Direct Voting Scheme

In this work, an approach combining local representations with a direct voting scheme on a k-nearest neighbors classifier to identify filled-in document images is presented. A document class is represented by a high number of local feature vectors selected from its reference image using a given criterion. In the test phase, a number of vectors are equally selected from an image and used to classify it. The experimental results show that the parameterization is not critical, and good performances in terms of error-rate and processing time can be obtained, even though the test documents contain a large proportion of filled-in regions, obviously not present in the reference images.